--- title: How UserJournAI works – From real behavior to activatable audiences description: Discover how UserJournAI turns aggregated behavioral signals into behavioral digital twins, activatable audiences, validated decisions, media activations and measurable campaigns. image: https://userjournai.com/hubfs/3.UJ-HOW.jpg --- [![UserJournAI](data:image/png;base64,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)](https://userjournai.com/en/home?hsLang=en) - [Platform](https://userjournai.com/en/platform?hsLang=en) - [How it works](https://userjournai.com/en/how-it-works?hsLang=en) - [Use cases](https://userjournai.com/en/use-cases?hsLang=en) - [Retail](https://userjournai.com/en/use-cases/retail?hsLang=en) - [E-commerce](https://userjournai.com/en/use-cases/e-commerce?hsLang=en) - [Media agencies](https://userjournai.com/en/use-cases/media-agencies?hsLang=en) - [DOOH](https://userjournai.com/en/use-cases/dooh?hsLang=en) - [Resources](https://userjournai.com/en/blog?hsLang=en) [FR](https://userjournai.com/fr/comment-ca-marche) [EN](https://userjournai.com/en/how-it-works) [Platform access](https://platform.userjournai.com) [Request a demo](https://userjournai.com/en/contact?hsLang=en) [Platform](https://userjournai.com/en/platform?hsLang=en) [How it works](https://userjournai.com/en/how-it-works?hsLang=en) Use cases [Overview](https://userjournai.com/en/use-cases?hsLang=en) [Retail](https://userjournai.com/en/use-cases/retail?hsLang=en) [E-commerce](https://userjournai.com/en/use-cases/e-commerce?hsLang=en) [Media agencies](https://userjournai.com/en/use-cases/media-agencies?hsLang=en) [DOOH](https://userjournai.com/en/use-cases/dooh?hsLang=en) [Resources](https://userjournai.com/en/blog?hsLang=en) [Request a demo](https://userjournai.com/en/contact?hsLang=en) [Platform access](https://platform.userjournai.com) How it works # How UserJournAI turns data into marketing decisions UserJournAI’s value comes from its ability to turn scattered signals into actionable decisions. The platform follows a structured chain: collect, model, segment, validate, activate and measure. Each step reduces part of the uncertainty before media budget is committed. Collect Model Test Validate Activate Measure [Request a demo](https://userjournai.com/en/contact?hsLang=en) [View the platform](https://userjournai.com/en/platform?hsLang=en) Method ## A complete chain, from signal to measurable result UserJournAI turns observable behavior into qualified audiences, activatable personas and measurable insights through a continuous chain: collect, model, qualify, interact, activate and learn. 01 ### Collect aggregated behavioral signals Real signals UserJournAI combines several families of signals to understand collective audience dynamics: anonymized physical mobility, aggregated socio-demographic data, consumer panels and contextual signals. **Concrete example** Detect audiences that regularly visit premium sports areas, commercial city centers or specific mobility catchment areas on certain days of the week. **Platform output** - High-traffic areas - Mobility catchment areas - Key time patterns - Contexts of interest → Understand real behavior rather than theoretical profiles. 02 ### Detect behavioral dynamics Behavioral AI Behavioral AI identifies collective patterns: movement habits, life rhythms, affinities, contexts of interest and intent signals. **Concrete example** An audience may show a high likelihood of interest in premium outdoor products by cross-referencing visits to nature areas, weekend mobility, declared affinities and exposure to specific media environments. **Platform output** - Affinity scores - Interest probabilities - Audience dynamics - Contextual reading → Identify more relevant audiences before activation. 03 ### Build behavioral digital twins Modeling Signals are turned into behavioral digital twins: probabilistic audience representations capable of modeling observable and aggregated collective behavior. The platform can then generate usable personas to interact with individually or run large-scale simulated studies in minutes. **Concrete example** Compare several similar segments, generate representative personas, then interact with them one-to-one or run a simulated study to identify reactions, expectations or intentions before delivery. **Platform output** - Generated personas - Behavioral segments - Simulated audience studies - One-to-one interactions with personas → Test hypotheses and explore audience reactions before investment. 04 ### Qualify intent before activation Qualification The platform helps identify the most relevant audiences, favorable contexts, key moments and most coherent messages before delivery. Teams can also interact with generated personas to test hypotheses, messages or marketing scenarios in minutes. **Concrete example** Determine whether an audience is more receptive during the week, on weekends, around a commercial area, in a home-to-work mobility context or in a specific media environment. **Platform output** - Priority audiences - Activation contexts - Messages to test - Priority areas → Validate decisions before committing media budgets. 05 ### Turn audiences into media activations Media activation Qualified audiences can be activated directly through the platform across compatible channels: DOOH, social ads, open web, CTV or geolocated campaigns depending on the use case. **Concrete example** A “premium outdoor sports” audience can be activated in DOOH around specific mobility areas, then extended to social ads, open web or CTV depending on the campaign objective. **Platform output** - Activatable audiences - Media recommendations - Priority contexts - Compatible direct activation → Move from insight to activation without disruption. 06 ### Measure real impact and enrich the models Measurement & learning Campaign results feed the platform to improve audience understanding, future activations, behavioral models and the quality of marketing decisions. **Concrete example** Identify the most responsive areas, the most effective contexts and the most engaging segments to adjust future activations. **Platform output** - Performance scores - Optimization insights - Results by audience - Continuous learning loop → A platform that learns over time. Data sources ## Real signals, not theoretical profiles UserJournAI combines several families of signals to build a more reliable understanding of audiences and their behavior. **Anonymized physical mobility**Flows, high-traffic areas and mobility catchment zones **Aggregated socio-demographic data**Territorial context, IRIS/INSEE data and area characteristics **Consumer panels**Affinities, intentions and structured declared behavior **Contextual signals**Trends, media environments and interest signals Privacy ## Understand audiences without identifying individuals The platform analyzes collective dynamics based on aggregated and anonymized signals. Personas and behavioral digital twins represent audiences, not real people. 01 ### Aggregated data A collective reading of behavior, areas, contexts and audience dynamics. 02 ### No individual identification No email, phone number, nominative data or re-identification logic. 03 ### Simulated personas Audience representations used to explore, test and compare hypotheses. 04 ### Privacy-first An approach designed for the post-cookie era and GDPR compliance requirements. Difference from traditional tools ## An upstream understanding and validation layer Traditional tools often start with activation and learn afterwards. UserJournAI adds an upstream understanding and validation layer. This is what turns a risky campaign test into a better-prepared decision. [Method **Platform** Explore the UserJournAI platform, its analysis, qualification, activation and measurement capabilities. Discover →](https://userjournai.com/en/how-it-works?hsLang=en) [Applications **Use cases** See how UserJournAI applies to retail, e-commerce, media agencies and DOOH. Explore →](https://userjournai.com/en/use-cases?hsLang=en) [Compliance **Privacy** Discover the privacy-first approach, without individual personal data and GDPR-ready. Understand →](https://userjournai.com/en/privacy?hsLang=en) Frequently asked questions ## Key questions to understand how it works Clear answers about data, behavioral AI, behavioral digital twins, activation and measurement. How do the personas generated by the platform work? UserJournAI can generate personas from behavioral digital twins to help teams interact with representative profiles, test messages, explore scenarios or run simulated audience studies in minutes. What data does UserJournAI use? UserJournAI combines anonymized physical mobility signals, aggregated socio-demographic data, consumer panels and contextual signals to understand audience dynamics without relying on theoretical profiles. How does behavioral AI work? Behavioral AI connects observable signals: visited areas, time patterns, affinities, contexts and structured declared behavior. It helps detect patterns and qualify audiences before activation. What are behavioral digital twins? They are probabilistic audience representations built from aggregated collective behavior. They make it possible to model audience dynamics, identify intent and improve the relevance of marketing decisions. How are audiences activated? Once qualified, audiences can be activated directly through the platform across compatible channels: DOOH, social ads, open web, CTV or geolocated campaigns depending on the use case. How is impact measured? Depending on the setup, UserJournAI can track exposure, visits, leads, sales or other indicators defined with marketing teams to improve future activations. Does UserJournAI use personal data? The platform relies on aggregated and anonymized data. It aims to understand collective audience dynamics, without individual identification. How long does it take to get usable audiences? The platform can quickly produce usable audiences and insights, depending on the scope, available sources and use case being studied. ## Understand before activating Discover how UserJournAI turns aggregated signals into usable audiences, validated decisions and more precisely activatable campaigns. [Request a demo](https://userjournai.com/en/contact?hsLang=en) [Explore use cases](https://userjournai.com/en/use-cases?hsLang=en) [![UserJournAI](data:image/png;base64,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)](https://userjournai.com/en/home?hsLang=en) UserJournAI helps teams understand real behavior, validate marketing decisions and activate higher-performing campaigns from launch. [YouTube](https://www.youtube.com/@UserJournAI)[Facebook](https://www.facebook.com/Userjournai)[LinkedIn](https://www.linkedin.com/company/userjournai/)[Instagram](https://www.instagram.com/userjournai/)[X](https://x.com/UserJournAI) Member of the collective [Hub&Up](https://www.hubandup.com) ### Company - [About](https://userjournai.com/en/about?hsLang=en) - [Manifesto](https://userjournai.com/en/manifesto?hsLang=en) ### Resources - [Blog](https://userjournai.com/en/blog?hsLang=en) - [Privacy](https://userjournai.com/en/privacy?hsLang=en) - [FAQ](https://userjournai.com/en/faq?hsLang=en) [Legal notice](https://userjournai.com/en/legal-notice?hsLang=en) — Copyright © 2026, User JournAI ```json { "@context" : "https://schema.org", "@type" : "Organization", "name" : "UserJournAI", "sameAs" : [ "https://www.youtube.com/@UserJournAI", "https://www.facebook.com/Userjournai", "https://www.linkedin.com/company/userjournai/", "https://www.instagram.com/userjournai/", "https://x.com/UserJournAI" ], "url" : "https://userjournai.com" } ```